[NeurIPS2024 Spotlight] Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
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Updated
May 14, 2025 - Python
[NeurIPS2024 Spotlight] Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
Reproduce some methods in semi-supervised papers.
RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT
Mean Teacher-based Cross-Domain Activity Recognition using WiFi Signals, IoTJ 2023
Implementation of semi-supervised learning: UDA, MixMatch, Mean-teacher, focusing on NLP, powered by Pytorch
PyTorch-driven model for efficient vascular segmentation and classification using limited data. Combines semi-supervised and supervised techniques, setting a new standard in resource-efficient auto-segmentation.
Experiments on some existing Re-ID methods on a different dataset with qualitative and quantitative analyses of their performance along with proposals to improve the results further.
Semi supervised learning for semantic image segmentation
The Mean Teacher Model is a popular approach for semi-supervised learning, where a student model learns from a more stable teacher model that updates through Exponential Moving Average (EMA). It helps improve consistency between predictions and provides a smoother training signal.
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